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Accenture

Advises businesses and runs their technology

AI Native Software Engineering Manager - Reinvention Center

Engineering LeadershipPosted on Nov 12
Full-time

About the Job

Requirements

  • A minimum of 8 years of professional software engineering experience building and operating production systems.
  • A minimum of 5 years of hands-on experience spanning multiple layers of the stack, including front end or API through services, integration, persistence, and asynchronous processing.
  • A minimum of 5 years of programming experience in at least one major language such as Java, Python, Go, TypeScript/JavaScript, or C#.
  • A minimum of 3 years of hands-on production experience with a major cloud platform (AWS, Azure, or GCP), including containers and orchestration (Docker, Kubernetes) and infrastructure as code (Terraform, Helm, or equivalent).
  • A minimum of 3 years of experience designing and operating CI/CD pipelines through to production, including automated testing and integrated security controls.
  • A minimum of 1 year of hands-on experience designing, building, and deploying AI-enabled or agentic systems in production or near-production environments, including model integration, tool use, retrieval, context engineering, orchestration, and evaluation.
  • A minimum of 1 year of experience applying AI-native engineering tools and workflows to day-to-day software development, including code generation, automated testing, debugging, modernization, and documentation.
  • A minimum of 3 years of experience leading engineering teams or significant technical workstreams, including leading client-facing technical discussions, workshops, or delivery sessions under ambiguity.
  • A bachelor's degree or equivalent work experience (minimum 12 years) is required. With an associate's degree, a minimum of 6 years of work experience is required.

Responsibilities

  • Design and build production systems across front end, APIs, backend services, integration, data, infrastructure, and AI components, making architectural decisions across these concerns together.
  • Diagnose production issues across application code, networks, infrastructure, identity, data, and AI workloads.
  • Design and deploy workloads using containers, Kubernetes, serverless, managed cloud services, and infrastructure as code (Terraform, Helm, or equivalent), building for elasticity, resilience, and recoverability.
  • Use the native capabilities of AWS, Azure, or GCP, including cloud networking, compute, storage, identity, secrets, service communication, and workload isolation.
  • Build the automated path from commit to production, including CI/CD, automated testing, security scanning, artifact management, infrastructure deployment, policy enforcement, and release controls.
  • Apply secure-by-design practices, including identity, least privilege, secrets, encryption, dependency and supply-chain security; instrument logs, metrics, traces, and SLOs; and lead incident response and root-cause analysis.
  • Design and implement the transactional, streaming, analytical, and unstructured data flows the solution needs across relational and non-relational stores, object storage, event platforms, and pipelines.
  • Design the data and context architecture that AI systems depend on, including retrieval, embeddings, metadata, knowledge sources, and context management, with quality, lineage, access, retention, and privacy engineered in.
  • Design and implement AI capabilities using frontier and enterprise models, including OpenAI, Anthropic, Microsoft, Google, and AWS models, integrated into live enterprise systems and production workflows.
  • Build agentic systems with tools, memory, context, planning, human intervention, and policy-controlled execution, along with evaluation frameworks, AI observability, and production safeguards.
  • Use AI-native development techniques throughout the lifecycle, including code generation, testing, debugging, modernization, documentation, analysis, and operations; design engineering workflows in which agents can safely perform bounded development and operational tasks.
  • Identify where AI materially improves engineering throughput, quality, or system operations, and establish the context, tools, permissions, evaluations, and human controls needed to use it safely.
  • Lead multidisciplinary teams spanning software, cloud, platform, security, data, and AI engineering; set engineering standards and develop engineers.
  • Facilitate architecture and engineering sessions with senior client technologists, and communicate trade-offs, risks, and recommendations to technical and executive audiences.
  • Convert lessons from delivery and production failures into reusable patterns, tooling, automation, and standards that influence internal assets and client roadmaps.
  • Contribute to internal communities of practice around AI-native and agentic engineering.
  • Partner directly with client technology leaders as an engineer and trusted advisor; turn ambiguous requirements into architectures, executable increments, and measurable outcomes, then get them into production.

Desired Qualifications

  • Build multi-agent orchestrations using frameworks such as LangGraph, Crew AI, the Claude Agent SDK, or the OpenAI SDK.
  • Have a public repository, portfolio, or open-source contribution featuring agents, tools, or plugins built by the candidate.
  • Design model and provider abstraction layers covering routing, fallback, latency, throughput, and cost management across multiple AI providers.
  • Define enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry and stream-based architectures.
  • Hold relevant cloud, security, or AI certifications.
  • Deliver AI-native solutions across more than one industry, adapting workflows to domain-specific processes and constraints.
  • Apply AI-assisted engineering to modernize an existing legacy estate.
  • Drive execution across multiple concurrent workstreams while maintaining quality, delivery, and alignment with client outcomes.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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